AI - IBM Data Lake

Empowering businesses to analyze any data at scale and in real-time through IBM Data Lake's efficient, scalable, and secure data lakehouse solutions.

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About IBM Data Lake

IBM Data Lake is a data management solution that empowers businesses to analyze any data in an open data lakehouse. They develop centralized repositories for managing large data volumes, acting as foundations for collecting and analyzing structured, semi-structured, and unstructured data. These data lakes and data lakehouses enable processing of various formats such as video, audio, logs, texts, social media, sensor data, and documents to power applications, analytics, and AI.

IBM Data Lakes and Data Lakehouses are efficient, scalable, and reduce data management complexity by delivering business value and providing the right data at the right time, regardless of the deployment environment - cloud, hybrid, or on-premises. They offer built-in governance and metadata management for data privacy and security while managing centrally and deploying globally with enterprise-wide governance solutions.

IBM Data Lakehouse Approach, represented by Watsonx.data, offers a new strategic approach to analytics and AI at scale using an open lakehouse architecture and supporting querying, governance, and open data formats. Enterprises can connect to their data in minutes, gain trusted insights quickly, and reduce their data warehouse costs with Watsonx.data, which is now available as a service on IBM Cloud and AWS and as containerized software.

IBM Data Lakehouse solutions include IBM Db2 for handling transactional, operational, and analytic data in mission-critical environments and IBM Netezzaยฎ for achieving simplicity, scalability, speed, and sophistication. They support all types of data and use cases with open source, open standards, and interoperability with IBM and third-party services. With their approach, businesses can drive down analytics costs by utilizing lower cost compute and storage and fit-for-purpose analytics engines that dynamically scale up and down, pairing the right workload with the right analytic engine.

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